Bank Lending Channel of

Monetary Transmission in Indonesia

Juda Agung* Rita Morena

Bambang Pramono Nugroho Joko Prastowo

Directorate of Economic Research and Monetary Policy

BANK INDONESIA

2001

_________________________

* The authors thank Hartadi A Sarwono, Perry Warjiyo, Sjamsul Arifin, Wibisono, and Sri

Liani Suselo for their consistent encouragement and thoughtful comments to this

study. Of course, any errors and ommisions are our responsibility. The findings and

conclusions in this study are those of the authors and do not necessarily represent the

views of Bank Indonesia.

1

Abstract

Using a battery of tests, the study investigates the existence of bank

lending channel of monetary transmission in Indonesia before and after crisis.

Aggregate evidence shows that a monetary policy is able to affect bank

lending with a lag due to ability of banks to insulate the decrease in deposits by

liquidating their securities holdings. Disaggregate evidence show that in the

aftermath of a monetary policy shock, there is a „flight to quality‟ of deposits

especially from private domestic banks to foreign banks and state banks and

„flight to quality‟ of bank lending from individuals to firms. Results of panel data

estimation demonstrate that effect of monetary policy is stronger for low capital

banks. Furthermore, survey on banks and firms support the econometric results.

We also found that that efficacy of a monetary policy, particularly a monetary

contraction, in influencing the bank lending is stronger in the period of post crisis

than prior to the crisis. This findings lends support indirectly existence of

asymmetric effect of a monetary policy: the stronger in the recession than in the

boom periods, stronger for low capital banks than low capital and for less

creditworthy borrowers.

JEL Classification: E44, E50

Key words: bank lending channel, bank portfolio behaviour, monetary

transmission

2

1. Introduction

Monetary policy in Indonesia is recently being faced with the most

challenging time. The monetary policy to control high pressure of inflation

and smooth out volatility of Rupiah exchange rate has been constrained

by banks and firms‟ financial restructuring process. Under such

circumstances when disintermediation of banking system takes place,

efficacy of monetary policy has been declining and sometimes the policy

is seen by many observers as costly.

The problems have been aggravated by the uncertainty regarding

the way in which the monetary policy affects the real economy in the

aftermath of the crisis. Attempts have been made to understanding the

monetary policy implications of current the banking crisis. A study by

Agung, et al (2001) to understand the existence of financial

disintermediation in the banking sector in the aftermath of the crisis and its

monetary policy implications is one of the attempts. However, a broader

agenda to understand the whole picture of monetary transmission

mechanism needs to be done. This agenda is of paramount as the full

implementation of the inflation targeting framework requires a deep

understanding on the work of the monetary policy in the economy both

in the short-run and long-run.

This paper is a part of the research agenda on the monetary

transmission mechanism in Indonesia. In the recent years, a large body

of literature has been developed on the efficacy of monetary policy and

channels through which the policy affects the real economy. Traditionally,

monetary policy is believed to influence the economy through money or

short-term interest rate which in turn affect the long term interest rate and

cost of capital and thus investment. For example, in a monetary

3

contraction, the banks reserves decrease and due to the reserve

requirement the ability of banks to issue the deposits were constrained. As

a result, the depositors hold less money (bank deposits) in their portfolios.

If prices are sticky, real money balance will fall and both short-term

interest rates and (through expectational effect) the *long-term interest

rates will rise. Accordingly, demand for loans, investments and interest-

sensitive spending such as housing, all fall.

For the last decade, there have been growing a voluminous studies

of the effects of imperfection in financial markets on the real economy

and business cycles (see e.g. Gertler, 1988; Bernanke and Gertler, 1989).

The understanding of the role of the financial market imperfection has

also generated theories on monetary transmission mechanism which

emphasize the importance of this imperfection, especially asymmetric

information problem in credit market, in explaining the effects of a

monetary policy. These theories can be categorized as the „asymmetric

information based transmission mechanism‟ or credit channel. There are

two strands of literature on the credit channel. First, the bank lending

channel which emphasize the effects of monetary policy on bank

balance sheet, especially in the asset side of banks. Second, the balance

sheet channel which emphasize the effects of monetary policy on firm

balance sheet and thereby access to banks‟ credit.

According to the bank lending channel, banks participate in the

transmission of monetary policy not only via their liabilities side but also

through their assets. For example, in a monetary contraction, banks

reserves decrease and owing to reserve requirements, bank deposits fall.

Should the decrease in bank deposits be not offset by other funds which

are free from reserve requirements, or by a decrease in securities, the

consequence would be a fall in bank loans. If bank loans also fall and

4

bank dependent borrowers are dominant in the economy, the restrictive

monetary policy results in a fall in investment and economic activity.

Hence, monetary policy not only directly influences the real interest rate

but also directly affects the supply of bank loans. Thus, two necessary

conditions for the existence of this channel are: (1) bank loans and

securities must be imperfect substitutes for some borrowers, or some

borrowers are bank dependent; (2) the central bank must be able to

constrain the supply of bank loans. While the first condition is likely to be

satisfied as the bank lending is still the dominant source of funds for firms‟

financing, the second condition is subject to empirical investigation. Using

sample data from 1985-1995, Agung (1998) proved that a monetary

policy was able to influence the bank supply of credit, in particular, of

small banks, not large banks which were able to shield their bank loan

supply by finding the cheaper source of funds from abroad.

This paper investigates the bank lending channel of monetary

transmission using the sample data including the period after the crisis and

using various tools to analyse. This is stimulating, at least for two grounds.

First, the evidence of ability of large banks to protect the lending supply

by accessing non deposit funds from abroad may be questioned recently

as the access to foreign funds has been very limited. Second, the

existence of credit crunch (Agung, et al., 2001) supports the bank lending

channel, i.e., the credit market is more supply-determined, rather than

demand-determined as suggested by the money/interest-rate channel.

However, the existence of credit crunch in which the non-price rationing

exist, simultaneously shows that the effectiveness of monetary in

influencing the supply of credit has also been reduced.

A battery of tests will be utilised to analyse the bank lending

channel. First, we use VAR approach as Bernanke and Blinder (1992) using

5

aggregate data to see effects of monetary policy on bank balance

sheets. However, the empirical studies using aggregate data suffer from

identification problem: the inability to establish whether the decline in

credit as a result of a monetary contraction stems from a decline in loan

supply or driven by the fall in demand for loans as a result of the high long

term interest rate as predicted by the interest rate channel. Accordingly,

following Kashyap and Stein (1995) we also use disaggregated data to

deal with this identification issue. The use of the disaggregated data,

hypothesis underlying the bank lending channel can be analysed. That is,

following a monetary contraction, smaller banks which do not have

access to other source of funds will decrease their loan supply more than

that of large banks. On the borrower side, small borrowers which

presumably are characterized by stronger informational asymmetries and

lower access to alternative source of funds should be more sensitive to

monetary contraction (Gertler and Gilchrist, 1993, 1994). As

complementary to the VAR analysis, we estimate long-run demand and

supply equation of the Indonesian credit market derived from vector error

correction model (VECM) following Kakes (2000) in order to identify

whether adjustment toward the equilibrium in the credit market is

dominated by supply as suggested by lending channel. Finally,

disaggregated evidence is analysed using bank level panel data to

examine whether a monetary shock generates differential effects across

banks according to their net worth (capital) position.

The remainder of this chapter is organised as follows. Section 2

reviews the role of banks in the monetary transmission mechanism. Section

3 presents the empirical results of the VAR analyses. Section 4 reports on

the empirical results of the credit market model for Indonesia. Section 5

6

presents the evidence from panel data regression. Section 6 reports the

survey results. Finally, a summary and conclusions is presented in section 7.

2. The role of banks in monetary transmission

There is widespread agreement among economists that banks or

financial intermediaries have generally played an important role in

transmitting monetary policy to the real economy. But the precise the

role of banks is still debated. In the standard view, known as the money

or interest rate channel, banks play a special role on the liabilities side, i.e.,

the banking system creates money (liquidity) by issuing deposits1 and plays

no role on the assets side. In a monetary contraction, the banks reserves

decrease and due to the reserve requirement the ability of banks to issue

the deposits were constrained. As a result, the depositors hold less money

(bank deposits) in their portfolios. If prices are sticky, real money balance

will fall and both short-term interest rates and (through expectational

effect) the long-term interest rates will rise. Accordingly, demand for loans,

investments and interest-sensitive spending such as housing, all fall.2 So,

three crucial conditions must be satisfied for the existence of a money

channel are: (1) prices are sticky so that monetary policy can affect real

money balances, (2) short-term interest rates do influence long-term

interest rates; and (3) the latter do influence real investment expenditure.

According to the “bank lending” (Bernanke and Blinder, 1988)

monetary transmission mechanism, banks‟ assets as well as their liabilities

1 By making loans or buying bonds. 2 Alternatively, some have described the money view as the standard IS-LM model, thus

it does not require the role of banks. In the IS-LM, if banks did not exist, central banks

could buy and sell bonds from public. This would influence interest rates and hence real

investment expenditure.

7

play an important role. In a monetary contraction, banks‟ reserves

decrease and given reserve requirements, banks‟ deposits fall. If the

decrease in deposits is not offset by other funds which are not subject to

reserve requirements, or by a decrease in securities, this will result in a

decrease in bank loans. If bank loans fall and bank dependent borrowers

are dominant in the economy, real investment expenditure will fall. Since

bank loans in many countries, especially developing countries, remain the

main source of external finance for business enterprises, a disrupting of

bank loan supply can reduce the economic activity. The necessary

conditions for the existence of this channel are: (1) the central bank must

be able to constrain the supply of bank loans, (2) bank loans and

securities must be imperfect substitute for some borrowers.

In regard to the second condition, since asymmetric information in

financial markets in developing countries seems to be prevalent, some

class of borrowers find it difficult to issue securities. Banks play an

important role in overcoming the information problem in credit markets,

consequently many borrowers are substantially bank dependent.3 Thus

the second condition seems to be satisfied. As pointed out by Bernanke

and Gertler (1995), the first condition is questionable in empirical grounds,

that is, whether monetary policy can significantly influence the supply of

bank loans. As we discussed above, in order to limit the ability of banks

to extend their loans after monetary contraction, banks must not easily

issue another form of liabilities to replace lost deposits. In other words, all

components of bank liabilities (except capital) must be subject to reserve

3 Even in the countries like US where financial markets have been well established, the

number of bank dependent borrowers are substantial (see Himmelberg and Morgan,

1995).

8

requirement4. Of course, to some extent banks which have gone public

can issue new equity to generate loanable funds.5 However as argued

by Kashyap and Stein (1995), as long as the banks do not face a

perfectly

elastic demand for their managed liabilities, a bank lending channel will

operate. Some argue that the regulatory action of central banks can

also significantly influence bank loan supply. For example, much research

has focused on the effect of capital adequacy regulations on the banks‟

willingness to lend (see for example, Peek and Rosengren,1995a,b).

The third channel through which monetary policy might be

transmitted to the real economy is known as the balance-sheet channel.6

The basic idea of this theory is that monetary policy can affect borrower‟s

financial position or collateralisable net worth, thereby influencing the

costs of external finance, which in its turn affects a borrower‟s financial

decision to engage investment expenditure. Suppose central banks

conducts a monetary tightening which raises market interest rates. This

can directly influence the borrowers‟ financial position in two ways.7 First,

it can cause a deterioration of the assets prices of the borrowers, and

hence reduce the value of the collateral the borrowers hold. Second, an

increase in market interest rates raises the cost of servicing outstanding

short-term or floating debt and so reduces net cash flow. Due to

asymmetric information, moral hazard problem and bankruptcy law,

borrowers with lower net worth are less creditworthy since the lender must

4 However, even if the reserve requirement (RR) is equal over all class of liabilities, in

practice, RR can not be flexibly applied. Hence, at least in the short run banks can

escape from monetary tightening. 5 Since 1989 banks in Indonesia have been allowed to issue equity. 6 Some call it the “broad credit channel” or “net worth channel”. For theoretical

exposition of this channel, see Bernanke and Gertler (1989), Calomiris and Hubbard

(1990), and Bernanke, Gertler and Gilchrist (1996). 7 See Bernanke and Gertler (1995) for detailed discussion.

9

bear higher costs in the event of the project fails. In contrast, the higher is

a borrower‟s net worth, the greater is his collateral, hence the lower are

the monitoring costs borne by the lenders. Consequently, the lenders

impose a varying premium for external finance reflecting the cost of

monitoring and evaluation. Thus, under information asymmetries, the

internal finance of a new project is cheaper than external finance; and

monetary policy influences the „wedge‟ between the internal and

external finance. A monetary contraction will increase the wedge and

vice versa.

The second and third channels have similarities. Both theories

suggest that monetary policy can influence borrowers who have a limited

access to capital markets, and the bank dependent and the finance-

constrained borrowers to some extent are identical. The differences

between the two channels is that in the bank lending channel the

condition that monetary policy must be able to affect bank loan supply is

crucial. In contrast, in the balance sheet channel banks are not the

central player, instead what matters is any disturbance which can

influence the premium paid for external finance. Since the arguments of

these two channels are primarily based on credit (capital) market

imperfection, they two channels are often classified as the „credit

channel‟ or the „capital market imperfections channel‟.

3. Empirical Evidence from VAR Analysis

3.1. Data

We analyse monthly data over a sample that runs from 1991:01 to

2000:12. Most earlier studies of the bank lending channel employed

aggregate data, comparing the relationship between total bank loans

10

versus total deposits and the economic variables in the context of vector

autoregressions (see Bernanke and Blinder, 1992) or the relative

forecasting power of the two aggregates with respect to output

fluctuations (Ramey, 1993, Kim, 1999, among others). However, it is now

widely agreed that testing with aggregate data can generate a

misleading conclusion. First, the use of aggregate time series cannot

resolve the well-known identification problem, i.e. to distinguish whether

the credit contraction which typically follows the monetary tightening is a

result of the supply by banks, as argued by the bank lending channel, or

the fall in demand for bank loans stemming from a recession. Second,

testing the relative importance of the bank lending vs the money view by

comparing the information content of these two aggregates with respect

to output would be misleading (Bernanke, 1993). Due to bank balance

sheet constraints, aggregate money supply (liability side of banks) and

aggregate bank loans (assets side of banks) by construction, move

together although they are not identical. Thus the relative forecasting

power of these two aggregate variables does not provide any

information about monetary transmissions.

To identify the channel of monetary policy, recent studies (Kashyap

and Stein, 1995, 1997; Dale and Haldane, 1995, Kakes, 2000, for example)

have tended to use cross sectional data to determine whether there are

distributional effects of monetary policy across lenders and borrowers, as

predicted by the bank lending channel argument. On the lenders side,

the lending view suggests that a monetary policy shock should constrain

bank loan supply since banks cannot frictionlessly raise non-deposit funds

to make up for a shortfall in their deposits. But this will depend on the

ability of banks to insulate themselves from the shock. Small banks which

have relatively limited access to non-deposit funds such as securities issues

11

or foreign borrowings are expected to be more affected by the monetary

shock and to tend to cut their loan supplies immediately following the

shock. On the borrower side, small firms that have limited access to

external finance should be more sensitive to a monetary shock (Gertler

and Gilchrist, 1994). The use of cross sectional data, furthermore,

eliminates the banks‟ balance sheet constraints.

This study follows Kashyap and Stein (1995) by disaggregating banks

into different classes, reflecting their size and accessibility to non-deposit

funds: state banks which are large and foreign exchange licensed banks,

private banks and foreign and joint venture banks. The source of data is

from Banking Statistics Monthly Report. The data for each class of banks

include bank loans, deposits, non-deposit funds and securities holdings.

The loans are also disaggregated into class of borrowers, i.e. loans to

individual and private enterprises and disaggregated into different types

of use, i.e., investment and working capital (Appedix A provides detailed

definition of data used).

3.2. VAR specification

The effects of monetary policy shock on bank balance sheets and

economic variables are examined using the vector autoregression (VAR)

approach. Specifically, we use the standard semi-structural VAR

approach as suggested by Bernanke and Blinder (1992) instead of

structural VAR since we do not explicitly use a theoretical framework to

identify the innovations, but impose a causal ordering. A structural model

is a linear dynamic system of the following form:

By C L yt t t ( ) (1)

12

or in MA form:

y Lt t ( ) (2)

where (L)=[B-C(L)]-1. y is nx1 vector of endogenous variables in the

system including one policy variable and some non-policy variables. t is a

vector of structural shocks, including the monetary policy shock. B

represents the structural parameters of contemporaneous endogenous

variables and C(L) is kth degree matrix polynomial in the lag operator, i.e.

C(L) = C1L+C2L2+...+ CkL

k. t is an nx1 vector of structural shocks with zero

mean, orthogonal and variance-covariance matrix E(tt) = I.

Equation (1) can be written in a reduced form which can be

estimated by OLS as:

y A L y ut t t ( ) (3)

with E(utut) = . By noting A(0) = B-1 , from the structural model (1) and the

reduced form model (3) we obtain:

A(L)=A(0)C(L) (4)

and,

ut = A(0)t (5)

Accordingly,

E(utut) = = A(0)A(0) T (6)

From (2) we can obtain impulse-response functions, (L), to structural

shocks, t and (L) can be calculated from (3) and (5):

(L) = [I-A(L)]-1

A(0) (7)

In order to identify the structural model and structural shock, t we have to

determine the nxn elements of matrix A(0). As is known from OLS

13

estimates of (3) we can solve (6) for A(0) and then deduce t from (5).

However system (6) only provides n(n+1)/2, hence we need n(n-1)/2

additional restrictions for the identification. A convenient way to add the

n(n-1)/2 restrictions is to assuming A(0) is lower triangular and use the

Cholesky decomposition of the variance-covariance matrix (Sims,

1980). This restriction is equivalent to assuming that the residuals ut form a

recursive system. The ordering of the variables in the system, therefore,

affects the recursive chain of causality among the shocks in any given

period. The policy variable is placed first (for example, Sims, 1992) if we

assume that there is no contemporaneous feedback from non-policy

variables onto the policy variables. Thus, this equivalently assumes that

the monetary decisions are made without considering the simultaneous

evolution of economic variables. This assumption is plausible if data of

non-policy variables are not readily available. If we assume that the

policy variable responds to contemporaneous feedback from non-policy

variables but there is one period lag of feedback of the policy shock on

non-policy variables, the policy variable should be placed last. Given the

high frequency data (monthly) that we use in constructing VARs, hence

the existence of information lag from non-policy variables8, we prefer the

former identifying restriction. Nevertheless, as the correlations across

residuals (t) are very small, the ordering is actually not significant.9

In contrast to Agung (1998), in examining the effects of monetary

policy on the bank balance sheet, we include all bank balance sheet

components in a VAR, so that the interrelationship between the balance

sheet components can be evaluated. This approach follows McMillin

8 For example, interest rate data (the policy variable) is readily available, while non-

policy variables such as real output and price were available with a lag. 9 A rule of thumb is that if ij< 0.2 for ij, the ordering of variables in a VAR is not

relevant (Enders, 1995, pp.309).

14

(1996). Since such specification involves a VAR with many variables, while

our data is rather limited, we use only lag of 3. The systems we developed

are six-variable VARs with the following ordering: monetary policy

indicator, bank deposits, loans, output and prices. We use real GDP and

deflator GDP for output and prices, respectively, when aggregate loans

are used. In the VAR using disaggregated data, for loans to individual, we

use real consumption and consumer price index as the output and prices,

while for loans to private enterprises, we use the production index and the

wholesale price index. In the disaggregation based on the bank

categories, the balance sheet variables are deposits and loans of the

relevant banks.

Table 1. Unit Root Tests

Variabel Level First Diff.

Lag ADF Test Lag ADF Test

SBI Rates 6 -2.851*** 11 -3.771*

Interbank Rates 1 -2.196 1 -10.361***

Base Money 11 -1.549 11 3.347**

Exchange rate 9 -1.982 8 -3.783***

Real GDP 10 -2.018 12 -2.035

GDP Deflator 9 -1.896 6 -3.622***

Consumer PI 8 -1.603 7 -4.022***

Real Consumption 12 -0.841 11 -3.427**

Deflator Consumption 8 -1.489 7 -3.847***

Production Index 9 -1.319 8 -3.103**

Real Investment 7 -1.352 6 -7.295***

Deflator Investment 11 -1.746 10 -2.831***

Deposit - Comercial Banks 12 -2.501 1 -7.283*

Deposit - State Banks 2 -1.300 1 -6.285*

Deposit - Private Banks 1 -1.182 10 -3.529**

Deposit - FX Banks 11 0.361 10 -3.147***

Deposit - Foreign Banks 1 -3.067 1 -10.122*

Lending - Comercial Banks a 1 -1.269 12 -2.532

Lending - State Banks a 8 -1.011 9 -2.095

Lending - Private Banks 2 -0.888 9 -3.256**

Lending - FX Banks a 8 -3.062 1 -5.840*

Lending - Foreign Banks 10 -0.488 9 -2.825

15

Total Lending To Private Enterprises 8 -3.193* 8 -2.090

Lending To Individuals 3 -1.671 2 -3.528***

Inv.Lending Rates - Commercial.Banks 1 -1.646 1 -10.516***

Inv.Lending Rates - State Banks 7 -2.352 1 -9.583***

Inv. Lending Rates - Private Banks 8 -3.091** 11 -2.933**

Inv.Lending Rates - Foreign Banks 9 -3.162** 7 -3.288**

Invest. Lending - Commerc.Banks 8 -3.594** 12 -3.231**

Invest. Lending - State Banks 8 -3.266* 9 -2.951**

Invest. Lending - Private Banks 8 -2.701 8 -2.497

Invest. Lending - Foreign Banks 2 -3.163* 4 -4.325***

WC.Lending Rates - Commercial.Banks 10 -2.839* 9 -2.977**

WC.Lending Rates - state banks 7 -2.738* 9 -3.228**

WC.Lending Rates - Private Banks 7 -2.826* 9 -2.484*

WC.Lending Rates - Foreign Banks 4 -2.488 9 -3.211**

Work. Cap. Lending - Commerc.Banks 8 -2.791 4 -3.036**

Work. Cap. Lending - State Banks 5 -1.711 1 -8.660***

Work. Cap. Lending - Private Banks 1 -1.577 1 -6.253***

Work. Cap. Lending - Foreign Banks 8 -3.685** 12 -3.941***

Notes : For levels, time trend and constant were included in the tests, while for first-difference, only constant was

included.

Critical values: levels: 5 % (*) = -3.44, 1%(**)=-4.02; first-differences: 5%(*)=-2.88, 1%(**)=-3.47. a First difference of

these variables are significant at 10% without trend and constant

All variables are in log levels except for interest rates and were

tested for stationarity by Augmented Dickey Fuller (ADF) tests (see Table

1). In general, the results indicate that all were found to be I(1). In spite of

non-stationarity of data, Sims (1980) and Doan (1992) do not recommend

differencing the data prior to VAR estimation even if they contain unit

roots. Their argument is that differencing in order to assure stationarity will

„throw away‟ valuable information concerning the interrelationships of

the variables in the system such as the possibility of cointegrating

relationship. It should be noted that the emphasis of VAR analysis is to

trace the dynamic relationships among a set of interested variables, not

the parameter estimates. Therefore, the VARs were estimated with all

variables in levels. Tests of cointegration using Johansen maximum

likelihood suggest that all systems are cointegrated (Appendix B).

16

3.3. Measuring the monetary policy variable

The crucial part of the studies on transmission mechanism is how to

measure the monetary policy indicator. The literature on the identification

of monetary policy indicator suggests that there are some alternatives to

measure the indicators: the interest rate used by the central bank to

influence the money market such as the Federal Funds rate in US

(Bernanke and Blinder, 1992), Romers‟s dates of monetary tightness

(Romer and Romer, 1990), or some aggregates such as base money, total

reserves, non-borrowed reserves (Strongin, 1995 and Christiano,

Eichenbaum and Evans, 1996).

Agung (1998) uses the money market interest rate (interbank money

market) as the monetary policy variable by arguing that Bank Indonesia

often indirectly targets the interbank interest rates. An alternative is the

SBI rates which have been widely used as the benchmark by the market,

in particular since the banks‟ holding of SBIs increased dramatically. The

problem of using the SBI rates are the auction system has been changed

three times. Before 1993, Bank Indonesia targeted the quantity of SBI in

the auction (cut-off rate), but since 1993 the system was changed to stop-

off rate in which the monetary authority set the interest rates on SBIs and

market determines the quantity of SBIs. The stop-off rate system was

changed again into cut-off rate in 1998. In practice, however, a mix of

price and quantity targets has been frequently executed. Another

alternative is base money, which has formally been used by Bank

Indonesia as the operating target since 1998.

In order to choose the appropriate measure of monetary policy we

follow a simple approach suggested by Bernanke and Blinder (1992). In

this approach, the selection is based on the information content of the

policy variables in the reduced forms of various real variables such as real

17

GDP, consumer price index, production index, consumption and

investment. In addition to lags of the real variables and the policy

variables, the reduced form also incorporates other monetary aggregates

such as M1. We use lag of 12 for all independent variables in the reduced

form. Since the variables are I(1), an alternative specification is the

models are specified in the ECM model in which the variables are

specified in first differenced and lag of cointegrating relationship is

included in the model. Using this approach we can select the policy

variables based on the short run information content (significance of the

first differenced) and the long run information content (significance of the

ECM coefficient). The results suggest that in general SBI rates and PUAB

rates perform better than base money. However, it is difficult to select the

best policy indicator between the two interest rates. With regard to the

short run information content, PUAB rates perform better than SBI rates,

while in the ECM specification, the long-run information content of SBI is

superior to the PUAB rates. Hence we use the two policy indicators and

compare the results in the VARs.

Table 2. Information content of the monetary policy variables

Variables Level Level Level

Spec. First Diff. ECM Spec. First Diff. ECM Spec. First Diff. ECM

GDP 0.151 0.389 -0.010 0.016 0.009 -0.104 0.042 0,025 -0.034

(2.027) (3.394) (3.964)

Indeks Produksi 0.319 0.248 -0.053 0.298 0.229 -0.005 0.000 0.000 0.002

(2.143) (1.267) (1.569)

CPI 0.742 0.564 0.001 0.004 0.004 -0.053 0.030 0.009 0.018

(0.032) (1.351) (0.724)

ECM Spec. ECM Spec. ECM Spec.

Base Money SBI Rates Interbank Rates

3.4. Innovation Analysis

Figure 1 and 2 reports the impulse responses of variables in the VAR

to a monetary shock measured by the SBI rates for the whole period and

18

period before the crisis. Generally speaking, the adverse effects of a

monetary tigntening on the banks‟ balance sheet and macroeconomic

variables are much stronger than those before crisis.

Figure 1. Effects of a Monetary Shock (SBI rate)

A. Whole sample B. Before crisis

-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30

SBI RATE

-.02

-.01

.00

.01

5 10 15 20 25 30

DEPOSIT

-.05

-.04

-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30

LENDING

-.08

-.04

.00

.04

.08

5 10 15 20 25 30

SECURITIES

-.020

-.015

-.010

-.005

.000

.005

.010

5 10 15 20 25 30

GDP REAL

-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30

GDP DEFLATOR

-.002

-.001

.000

.001

.002

.003

.004

.005

.006

5 10 15 20 25 30

SBI RATE

-.008

-.004

.000

.004

5 10 15 20 25 30

DEPOSIT

-.004

-.002

.000

.002

.004

5 10 15 20 25 30

LENDING

-.06

-.04

-.02

.00

.02

5 10 15 20 25 30

SECURITIES

-.005

-.004

-.003

-.002

-.001

.000

.001

.002

.003

5 10 15 20 25 30

GDP REAL

-.003

-.002

-.001

.000

.001

.002

.003

.004

.005

5 10 15 20 25 30

GDP DEFLATOR

Before the crisis, bank lending is almost not affected by a tight

monetary policy. This result is consistent with findings by Agung (1998)

who also use pre-crisis data. One of a reasonable explanation of low

sensitivity of lending to a monetary shock is that before the crisis,

especially since the beginning of 1990s, the access of domestic

commercial banks to international source of funds was relatively easy.

Hence, in spite of tight money, they can still provide loans to their

borrowers. A survey conducted by Hadad (1996) also found similar

phenomenon. During the tight money period (e.g. in the aftermath of

what so called-Gebrakan Sumarlin), the loan growth of state banks and

19

large private banks was higher than their deposit growth. In fact,

domestic banks have been major issuers of bonds into international

markets during the period (World Bank, 1996). Large banks obviously have

better credit ratings than smaller banks and are thus able to raise funds

less expensively. This differential behaviour of state banks and private

banks is clearly reflected in Figure 3. Loans of state banks are completely

insensitive to a monetary shock, while that of private banks are more

sensitive to the shock.

The relatively high sensitivity of commercial bank lending for the

whole sample is partly influenced by behaviour of bank lending during

and after the crisis. Given weakening of firms‟ balance sheet amidst low

economic prospect, a monetary tightening worsens the firms‟ financial

position and raises the probability of default and hence reduces the

willingness of bank to lend. This is consistent with a recent study by

(Agung et al., 2001) who found the existence of „credit crunch‟ in the

aftermath of the crisis. Under such situation, they argue, a tight money

exacerbates the unwillingness of banks to lend. This is also confirmed by a

corresponding study on balance sheet channel that concludes the

existence of financial accelerator effect of monetary policy, especially

after the crisis. Similar impulse responses is obtained if we use the PUAB

rate as the policy variable, although the effect of a change in SBI rate

seems to be more pronounced than a change on PUAB rate.

For the whole period sample, although the bank lending is

responsive to a monetary shock, its response is rather slow, i.e. about 10

months for bank lending to decline after a shock occurs. Another bank

asset portfolio, the securities holdings of commercial banks, immediately

fall after a shock and take about 10 months to return back. This

behaviour can be interpreted as an indication that banks prefer to use

20

their securities holdings as a buffer stock to offset monetary shocks. This

behaviour is consistent across different types of banks, except for the

foreign and joint-venture banks (Figure 3). This is not surprising that after a

monetary contraction there is an indication of flight to quality of deposits,

especially from private domestic banks to foreign banks. While deposits

of private banks fall immediately after a shock, deposits of foreign banks

increases (Figure 3). Accordingly, the foreign banks have an ample

deposit funds to maintain the credit line without liquidating their securities

holdings.

Figure 2. Effects of a Monetary Shock (PUAB rate)

A. Whole sample B. Before crisis

-.04

-.02

.00

.02

.04

.06

.08

5 10 15 20 25 30

INTERBANK RATE

-.02

-.01

.00

.01

.02

.03

.04

5 10 15 20 25 30

DEPOSIT

-.08

-.04

.00

.04

5 10 15 20 25 30

LENDING

-.10

-.05

.00

.05

.10

.15

.20

5 10 15 20 25 30

SECURITIES

-.03

-.02

-.01

.00

.01

5 10 15 20 25 30

GDP REAL

-.02

-.01

.00

.01

.02

.03

.04

.05

5 10 15 20 25 30

GDP DEFLATOR

-.002

.000

.002

.004

.006

.008

.010

.012

5 10 15 20 25 30

INTERBANK RATE

-.008

-.004

.000

.004

.008

5 10 15 20 25 30

DEPOSIT

-.002

-.001

.000

.001

.002

.003

.004

.005

.006

5 10 15 20 25 30

LENDING

-.06

-.05

-.04

-.03

-.02

-.01

.00

.01

.02

.03

5 10 15 20 25 30

SECURITIES

-.006

-.004

-.002

.000

.002

.004

.006

5 10 15 20 25 30

GDP REAL

-.002

-.001

.000

.001

.002

.003

.004

.005

.006

5 10 15 20 25 30

GDP DEFLATOR

The lag of bank lending to respond a shock can be attributed to

the fact that bank lending practices, especially investment loans, are

mostly conducted on a loan commitment basis, instead of on a project or

21

fixed-term basis. Under such a commitment, banks allow borrowers to

draw down a line of credit at their discretion; and borrowers pay a fee for

the credit line and pay interest on actual loans that have been drawn. As

a result of this system, banks cannot prevent the borrowers from drawing

the credit even when the monetary condition is tightened. Banks can

only reduce the supply of new loans, which presumably does not

immediately lead to a substantial fall in aggregate loans.

Figure 3. Effects of a monetary shock to balance sheet of different types

of banks: the whole sample

A disaggregation of total bank loans into corporate lending and

individual (household) lending (Figure 4), however, suggests that the

insignificant response of aggregate lending stems from the loan to firms.

By contrast, the loans for individuals drop significantly in the aftermath of

monetary shock. This may be explained by what so-called the „flight to

quality‟ phenomenon as suggested by Bernanke, et al. (1996). That is, in a

monetary contraction, to compensate the decline in cash flow, the

-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

5 10 15 20 25 30

DEPOSIT STATE BANKS

-.08

-.04

.00

.04

.08

5 10 15 20 25 30

LENDING STATE BANKS

-.2

-.1

.0

.1

.2

.3

.4

5 10 15 20 25 30

SECURITIES STATE BANKS

-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

5 10 15 20 25 30

DEPOSIT PRIVATE BANKS

-.08

-.04

.00

.04

.08

5 10 15 20 25 30

LENDING PRIVATE BANKS

-.2

-.1

.0

.1

.2

.3

.4

5 10 15 20 25 30

SECURITIES PRIVATE BANKS

-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

5 10 15 20 25 30 35

DEPOSIT FX BANKS

-.08

-.04

.00

.04

.08

5 10 15 20 25 30 35

LENDING FX BANKS

-.2

-.1

.0

.1

.2

.3

.4

5 10 15 20 25 30 35

SECURITIES FX BANKS

-.03

-.02

-.01

.00

.01

.02

.03

.04

.05

5 10 15 20 25 30

DEPOSIT FOREIGN BANKS

-.08

-.04

.00

.04

.08

5 10 15 20 25 30

LENDING FOREIGN BANKS

-.2

-.1

.0

.1

.2

.3

.4

5 10 15 20 25 30

SECURITIES FOREIGN BANKS

22

creditworthy borrowers have access to short-term loans, while loans to the

less creditworthy borrowers such as individuals or small firms will be

rationed.

Figure 4. Effects of a Monetary Shock (SBI rate): Corporate vs Individual

Loans

A. Lending to Corporate B. Lending to Individuals

-.06

-.04

-.02

.00

.02

.04

.06

5 10 15 20 25 30

LENDING TO FIRMS

-.06

-.04

-.02

.00

.02

.04

.06

5 10 15 20 25 30

LENDING TO INDIVIDUALS

Figure 5. Effects of a Monetary Shock (SBI rate): Working capital vs

Investment Loans

A. Investment Loans B. Working capital Loans

-.04 -.03 -.02 -.01 .00 .01 .02 .03 .04 .05

5 10 15 20 25 30

-.04 -.03

-.02 -.01 .00

.01

.02

.03

.04

.05

5 10 15 20 25 30

23

4. Evidence from Adjustment in the Credit market

4.1. Methodology

The hypothesis of the bank lending channel is that a monetary

policy affects the supply of bank lending which in turn influence the

investment by bank-dependent borrowers. The crucial assumption

underlying the hypothesis, thus, the credit market is supply-determined.

Following the approach conducted by Kakes (2000), we utilize the

Johansen‟s cointegration framework and impose the restrictions on the

cointegrating parameters representing the long run supply of and

demand for credit and to examine whether the short run adjustment

toward equilibrium is dominated by the demand or supply determined. If

the system is dominated by supply, one would expect that initially the

adjustment mainly takes place in the direction of the supply equation,

while eventually both relationships are satisfied.

The model of credit market is developed in a four-variable VECM.

The supply of bank lending is a function of the spread between the bank‟

lending rate and bank‟ funding costs proxied by the deposits rate, and

the level of economic activity. Whereas, the demand for loans is a

function of the lending rate and the level of economic activity. Thus, the

VECM includes the following variables: bank lending, level of economic

activity, loan rate and deposit rate. We analyse a VECM of the working

capital credit and investment credit markets as well as the disaggregation

according to bank categories.

In testing the Johansen‟s cointegration framework, we use the so-

called Pantula principle (Johansen, 1991) to select the deterministic

components and the rank of the cointegration matrix . The results

24

suggest that the rank of r = 2 which implies that we have to find two long-

run relationship in order to identify the cointegration space. Table 3 also

shows that we use model 1, i.e., include a constant in the cointegration

relationship and allow for a trend in the levels of variables.

Table 3. Selection of the cointegration model: joint test of deterministic

specification and rank of long run matrix

Variables

trace

Model 1 Model 2 Model 3

Working Capital

r = 0 51,37** 49,52** 55,82**

r 1 33,33** 32,55** 34,80**

r 2 9,567 9,083 23,37*

r 3 2,392 0,495 9,074

Investment

r = 0 40,51** 38,55** 44,39**

r 1 31,05** 24,93* 26,39*

r 2 12,360 11,67 11,74

r 3 6,955 5,94* 7,419

4.2. Results

Table 4 provides the unrestricted cointegrating relationship, given

rank r = 2. The first cointegrating equation likely represents the supply

function, while the second cointegrating equation represent the demand

function. Table 4 also report the outcome of restriction. We impose two

restrictions: equality restriction in the supply equation, i.e., coefficients on il

equals to (-) coefficients on id and exclusion restriction, coefficient on id is

zero in the demand equation. After normalizing with respect to Lt, the

following long-run supply and demand relationship, respectively:

25

Market of working capital credit:

)(046.0166.2 d

t

l

tt

S

t iiyL (11)

l

tt

D

t iyL 016.0792.1 (12)

Market of investment credit:

)(008.0567.2 d

t

l

tt

S

t iiyL (13)

l

tt

D

t iyL 011.0473.2 (14)

The credit demand equation shows long-run income elasticities of 1.8 and

2.5 for working capital and investment loans, respectively, which are

comparable to studies in other countries (e.g. Kakes, 2000, Fase, 1995).

The interest elasticities are -0.36 (-0.016 x 22.5, i.e. product of semi-elasticity

of loan rate and sample mean of loan rate) and -0.20 (-0.011 x 18.18) for

working capital and investment loans, respectively. These results are very

intuitive that the demand for working capital loans (short term loans) is

more sensitive to a change in the loan interest rates. Similarly, the

elasticity of interest rate for working capital loans in the supply function is

higher than that for investment loans.

Table 4 shows the adjustment coefficients, , which indicate the

speed towards the long run equilibrium. Compare coefficient for supply

and demand equation for working capital loans, it appears that bank

loans adjust significantly in the direction of the long-run supply of credit (

= - 0.10), while the adjustment to the demand equation is insignificant with

speed of adjustment coefficient 0.04. The same conclusion is also found

for investment loans, i.e., coefficient of speed of adjustment for supply

equation is – 0.28, while that for demand equation is 0.19. This suggest

that in the short run the market for working capital and investment credit is

dominated by supply rather than demand.

26

Table 4. Results for cointegration and restricted cointegration

A. Working Capital Loans

[1] Test for the number of cointegrating vector

L Y i L i D

Eigenvalues 0,424407 0,301216 0,0977544 0,025388

Hypothesis r = 0 r 1 r 2 R 3

trace 96,66** 45,29** 11,96 2,39

max 51,37** 33,33** 9,57 2,39

[2] Standardized eigenvectors, ‟

L Y i L i D

Vector1 1,000 -6,079 -0,698 0,528

Vector2 -1,285 1,000 -0,238 0,161

[3] Standardized adjustment coefficient,

L Y i L i D

Vector 1 -0,016 0,002 0,134 0,004

Vector2 0,026 0,004 -0,001 -1,084

[4] Restricted eigenvectors

L Y i L i D

Supply (=-0.10) 1 -2.17 -0.05 0.05

Demand (=0.04) 1 -1.79 0.02 0.00

B. Investment Loans

[1] Test for the number of cointegrating vector

L Y i L i D

Eigenvalues 0,254408 0,201477 0,0857003 0,0491518

Hypothesis r = 0 r 1 r 2 R 3

trace 90,88** 50,37** 19,32 6,955

max 40,51** 31,05** 12,36 6,955

[2] Standardized eigenvectors, ‟

L Y i L i D

Vector1 1,000 -2,677 -0,022 0,013

Vector2 0,116 1,000 0,161 -0,066

[3] Standardized adjustment coefficient,

L Y i L i D

Vector1 -0,100 -0,009 0,313 0,172

Vector2 0,013 -0,011 -0,472 1,472

[4] Restricted eigenvector

L Y i L i D

Supply (=-0.28) 1 -2.57 -0.01 -0.01

Demand (=0.19) 1 -2.41 -0.01 0.00

27

5. Evidence from Bank Level Panel Data

As aforementioned previously, the existence of bank lending

channel should be tested using disaggregated bank data. In section 3

using VAR approach we have shown differential lending behaviour across

banks according to their accessability to non deposits funds and their

securities holdings to maintain their lending activities. While many studies

have used such VAR approach to investigate differential effects,

empirical evidence about the existence of bank lending channels using

bank-level data is scarce. Kashyap and Stein (2000) and Kishan and

Opiela (2000) for US and Bondt (1999) for European countries are the

notable exceptions. Using bank level data, Kashyap and Stein (2000)

found that sensitivity of bank lending to a monetary policy is determined

by buffer stock owned by banks. Lending of banks that have lower ratios

of cash and securities to assets are more sensitive to a monetary

tightening. Bondt (1999) using similar approach using bank level with

different size and liquidity to examine impact of monetary policy through

bank lending channel. Kishan and Opiela (2000) separate banks

according to their capital leverage ratio by arguing that capital‟s role in

absorbing shock to assets makes it an indicator of bank health and a

good indicator of bank‟s ability to raise funds during tight money policy.

In the following empirical approach, we combine both capital

(capital to asset ratio) and size (assets). We test empirically whether the

effects of monetary policy on bank lending are more pronounced for

small and low capital banks.

28

Methodology and Data

The empirical framework is as follows:

)*()*()*( 654321 ittitititititttiit AYYLCAPDEPDEPLCAPrrL

with index i referring to bank i and t to period t; L denotes log loans, r

denotes SBI or interbank (PUAB) rates, LCAP denotes low capital indicator

(1 for banks with Capital to Asset ratio below the sample first quartile and

0 otherwise), DEP denotes log deposits, Y denotes log real GDP, and A

denotes log total assets as a proxy of bank size.

We can expect that 1 < 0, that is, the an increase in interest rate on

SBI or PUAB, as the monetary policy indicator, will lead to a fall in bank

lending. The impact may differ across banks according to their capital

strength. The response of lending of low capital banks is expected to be

more sensitive to a change in interest rate, 2 < 0. Similarly, using deposits

as the proxy of the bank lending capacity (loanable funds), we can

expect that a higher the loanable funds the higher loan growth, 3 > 0.

The sensitivity of loan with respect to the loanable funds should be more

higher for low capital banks that may more difficult to find other sources

of funds, 4 > 0. Loan demand effects are assumed to be captured by

the growth rate of real GDP; higher economic activity will leas to a rise in

bank lending, 5 > 0. Assuming that bank and borrower size are positively

correlated, we can expect that the impact of loan demand to a

monetary shock may be stronger for small banks (Bondt, 1999) and hence

6 < 0.

Bank-level data are obtained from Monthly Bank Report, over

period 1994-1999. The sample size is 140 banks, all still exist until the end of

1999. In estimation, we split the sample estimation into before crisis, after

29

crisis and the whole sample to capture possible different behaviour. Table

5 summarizes the characteristics of data.

Table 5. Sample characteristics of banks data

Asset Capital Loan Deposit C/A

Before Mean 7,818,206 177,667 1,388,371 1,779,192 0.123

Crisis Median 446,002 41,013 220,792 140,900 0.101

1st quartile 149,001 15,815 81,088 51,403 0.063

After Mean 15,820,693 853,297 2,567,427 5,590,698 0.089

Crisis Median 1,131,292 61,182 420,213 301,733 0.071

1st quartile 299,345 22,947 82,115 94,578 0.033

Whole Mean 10,485,702 402,877 1,781,390 3,049,694 0.112

Period Median 575,147 51,009 248,567 187,872 0.092

1st quartile 180,021 17,410 81,421 61,322 0.051

Empirical Results

The results of estimation are presented in Table 6 and 7, for SBI and

PUAB rates as policy variables, respectively. Generally speaking, the

results are in line with our prior expectation. For the whole period, all

coefficients are significant. To test our hypothesis regarding the existence

of a bank lending channel, the significance level of a negative estimated

2, positive 4 is examined. The results support the hypothesis, that is, an

increase in interest of SBI or PUAB reduces the bank lending supply and

their effects are more pronounced for low capital banks. Furthermore, it

can be seen that bank lending supply is sensitive to lending capacity

available. What is more interesting result is that the sensitivity of bank

lending is higher for banks wit low capital. A significant negative

estimated 6 support the hypothesis that lending of small banks is more

sensitive to demand effect.

The next interesting findings are that there is differential behaviour

of bank lending before and after the crisis. Before the crisis, the interest

rate of SBI and PUAB do not significantly influence the bank lending. This

confirm to our VAR analysis above. However, during that period, lending

of banks with low capital is negatively affected by the monetary

30

tightening. Again, this result suggests that the bank lending channel is

operative through banks with low capital. This can also be interpreted as

a fact that bank lending channel is strongly working when the capital of

commercial banks is weak. After the crisis, the bank lending is sensitive to

a monetary shock though only significant at 10%. The sensitivities are also

higher for banks with low capital in spite of insignificant.

Table 6. Panel data results: SBI rate as the policy variable

Whole Sample

Before Crisis

After Crisis

Constant

0.01

(11.97)

0.01

(5.12)

-0.01

(-2.80)

r-sbit-1 -0.04

(-11.84)

0.002

(0.20)

-0.01

(-1.65)

r-sbit-1 * lowcap -0.01

(-3.14)

-0.03

(-4.18)

-0.01

(-1.20) dep 0.12

(37.19)

0.07

(18.80)

0.19

(30.62) dep * lowcap 0.09

(12.12)

0.03

(3.096)

0.07

(5.67) yt 1.24

(5.87)

1.15

(4.31)

0.89

(2.51) yt * Size -0.08

(-5.09)

-0.08

(-4.01)

-0.05

(-2.06)

Notes: Values in parentheses are t-statistics

Table 7. Panel data results: PUAB rate as the policy variable

Whole Sample (1994.01 – 1999.12)

Before Crisis (1994.01 – 1997.12)

After Crisis (1998.01 – 1999.12)

Constant

0.01

(10.14)

0.01

(8.73)

-0.01

(-3.29)

r-puabt-1

-0.03

(-9.89)

0.01

(1.57)

-0.01

(-1.07)

r-puabt-1 *

lowcap

-0.01

(-2.81)

-0.01

(-2.50)

-0.01

(-1.18) dep 0.13

(37.40)

0.07

(18.95)

0.195

(30.68) dep * lowcap 0.09

(12.09)

0.03

(2.75)

0.07

(5.68) yt 1.27

(5.95)

1.30

(4.89)

0.89

(2.48) yt * Size -0.08

(-5.54)

-0.09

(-4.54)

-0.05

(-2.14)

Notes: Values in parentheses are t-statistics

31

6. Evidence from survey

This section presents an analysis based on a survey on banks and

firms. The survey is designed to generate answers to some important

questions on behaviour banks and firms in the aftermath of a monetary

shock. From the banking survey, the main issue examined is whether

banks reduce their lending supply after a monetary shock, as expected

by the bank lending channel hypothesis. How do they reduce the

lending supply, by price or non-price mechanism? If they reduce their

loan supply with a lag, how do they maintain their lending supply? From

the firm survey, the issues examined are: what are sources of funds, how is

the sensitivity of demand for bank lending after a monetary tightening?

Are they rationed during a tight money periods?

6.1. Sample characteristics

The characteristics of the banks participating in the survey are

summarized in Figure 6. The number of banks interviewed was 28, which

can be categorized as follows: State Bank 14%, Private FX Bank 48%,

Private Non-FX Bank 7%, foreign & joint Venture Bank 32% and Regional

Bank 4%. According to asset size, the most respondents are relatively

large banks with around 65% of banks having more than Rp 10 triliuns.

Examining whether a bank is recapitalized after the crisis is important for

analysing their behaviour. Accodingly, we split the sample into

recapitalised banks (57%) and non-recapitalised bank (43%).

Private Non-

FX Bank

7%

Regional Bank

4%

State Bank

14%

Foreign &

Joint Venture

Bank

32%

Private FX

bank

43%

Rp10 triliun - Rp50

triliun

43%

More than Rp 50

triliun

18%

Less than Rp 1

triliun

14%

Rp1 triliun - Rp10

triliun

25%

Figure 6. Characteristics of Bank Sample

32

For the firm survey, we interviewed 141 companies, categorized

according to business sector and scale. According to business sectors,

the sample can be ctagorized into manufaturing sector (39%), trade

(31%), property and construction (14%), agriculture (6%) and other sector

(10%) (Figure 7). Meanwhile classified according to the size of turnover,

both large and medium firms have same portion 42%, but for small firm just

16% of total respondent. The majority of firms (63%) sell their product in

domestic market, and only 37% of firms have export orientation.

2. Are firms bank-dependent?

As outlined previously, the existence of the bank lending channel of

monetary transmission depends on whether the bank lending is a

dominant source of external funds. The survey indicates that in

conducting their business activities, the firms use internal fund as the main

source of financing (60,71%) (Figure 8). Meanwhile, as the source of

external financing, bank credit still serves as the main source of funds.

About 20,71% of firms use bank credits as the main sources of funds. This

finding is in line with a „credit crunch‟ survey conducted by Agung, et al.

(2001) and substantially different from results from surveys before the crisis

(e.g. Ang, Fatemi and Tourani-Rad, 1997). As found in many studies using

Property &

Construction

14%

Others

10%Manufacture/In

dustry

39%

Trade

31%

Agriculture

6%

Medium

Firms

42%

Small Firms

16% Large Firms

42%

Figure 7. Characteristic of Firm Sample

33

pre-crisis data, the banks are the main sources of funds or at least 40% of

firms‟ source of financing.

Firms using internal funds as the main sources consider funds from

head/business group (46%) and retained earnings (44%) as the main

sources. The incomes from deposit interest and foreign exchange profit

are only around 4%. Referring to credit crunch survey, the main reasons of

using internal fund are the relatively high loan rate, under utilized of their

own capital, tightness of credit procedures and the existence of banks

credit rationing.

Firms using bank loans as main source of financing come from

manufacturing sector 37,9%, while trade and property/construction have

the same portion about 20,7%, and agriculture sector only 13,8%.

Classified according to business scale, large firm 55,2%, medium firm 41,4%

and small firm only 3,4%. Small portion for agriculture sector and small

scale business because the respondent from those categories

Figure 10. Banks Loan as the main source of external fund

Figure 8. Sources of Funds Figure 9. Sources of Internal Funds

34

experiencing difficulty to obtain credit bank. Obstacles in obtaining bank

credit are tightness of collateral condition, declining cash flow, and credit

rationing.

6.2. Lending behaviour after a monetary shock

The necessary condition of the existence of bank lending channel is

whether or not a monetary policy influences the loans supply. The survey

indicates that in the case of tight money, the majority of banks (77%) will

reduce their loan supply and 23% of banks will not. As indicated by the

quantitative study, the foreign and joint-venture banks are less influenced

by the tight money than their domestic counterparts. The survey suggests

that 50% of foreign and joint-venture banks will reduce their loans in the

aftermath of the tight money policy. Meanwhile, all private non-foreign

exchange banks and regional banks reduce their lending supply. This

supports previous empirical findings (e.g. Agung, 1998) that small banks‟

reliance on the deposits as the source of funds makes their lending is more

sensitive to a monetary tightening. By contrast, foreign banks and larger

banks such as state banks and private foreign exchange banks that have

access to non-deposit funds (e.g. foreign funds) are able to shield their

lending supply from the shock. Furthermore, the banks‟ holdings of

securities enable them to protect their lending, at least in the short run.

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0%

State banks

FX Private domestic banks

Non-FX Private domestic banks

Regional banks

Foreign banks

Yes No

Figure 11. Reducing loan supply in the aftermath of monetary

shock?

35

In the case of monetary tightening reflected in an increase in the

SBI rate, banks reduce bank lending supply either by price mechanism,

through increasing the loan rate or tightening the credit conditions, and

non-price mechanism, through reducing new loans. Majority of banks

(71%) raise the loan rate in the aftermath of tight money and around

21.4% of banks reduce the loan supply. A more interesting result is that

banks that reduce the lending by rationing credit rather than by raising

the loan interest rates coming from the private banks and regional banks.

Meanwhile, state banks and foreign banks raising the interest rate in order

to reduce loan. The similar result is found in the case of monetary

loosening (a fall in SBI rate), that is, around 72% of banks reduce loan rates

and around 20% of banks raise the lending supply.

Table 8. Effects of monetary policy shock

Reduce in SBI rate Rise in SBI rate

Banks Reducing

loan

rates

Raising

new

loans Others Total

Raising

loan rate

Reducing

new

loans Other Total

State banks 2 1 1 4 3 1 1 5

50.0% 25.0% 25.0% 100.0% 60.0% 20.0% 20.0% 100.0%

Private FX banks 8 3 0 11 6 5 0 11

72.7% 27.3% 0.0% 100.0% 54.5% 45.5% 100.0%

Private Non-FX

banks 1 1 0 2 2 0 0 2

50.0% 50.0% 0.0% 100.0% 100.0% 100.0%

Regional banks 0 1 0 1 0 1 0 1

0.0% 100.0% 0.0% 100.0% 0.0% 100.0% 100.0%

Foreign and joint-

venture 7 0 1 8 9 0 0 9

87.5% 0.0% 12.5% 100.0% 100.0% 0.0% 0.0% 100.0%

Total 18 5 2 25 20 6 1 28

72.0% 20.0% 8.0% 100.0% 71.4% 21.4% 3.6% 100.0%

36

6.3. Lag in responses to a monetary tightening

As found previously that some banks do not reduce the loan supply

after a monetary tightening. This confirms the quantitative results that

suggest that the response of bank lending to a tightening of monetary

policy needs time lag. There are three possible reasons for the apparently

insensitive lending supply: (1) to maintain their relationship with their

borrowers; (2) to honour the loan commitments that have been made; (3)

to prevent the borrower‟s financial problem if the banks discontinue the

lending supply. The survey indicates that the main reason is to maintain

their relationship with borrowers and to honour loan commitments.

To finance the lending activities in the case of tight money, the

majority of banks liquidate their SBIs. This supports the empirical findings

that the banks‟ securities fall in the aftermath of the monetary tightening.

The second resort is borrowing from interbank market and selling their

bonds holdings.

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00%

Borrowing from interbank market

Borrowing from abroad

Selling SBI

Selling bonds

Figure 12. The reasons of not to reducing the lending suply after a shock

Figure 13. Source of funds to finance lendings after a monetary shock

37

6. Summary and Conclusions

We have investigated the existence of bank lending channel of

monetary transmission in Indonesia before and after crisis. Given existence

of „bank dependent borrowers‟ as the secondary condition of bank

lending channel clearly satisfied, our study particularly focuses on the first

condition of the bank lending channel to exist; that is, whether a

monetary policy affects the quantity of bank lending. We use three

different methods to achieve robust conclusions. First, using Bernanke-

Blinder type of VAR we examine responses of banks‟ balance sheet

(deposits, lending and securities holdings) to a monetary shock measured

by SBI rate and interbank rates. Second, using the restricted version of

VECM to identify supply of and demand for credit and examine whether

the short run adjustment toward equilibrium is dominated by the supply

determined as suggested by the credit channel hypothesis. Third, we use

bank-level panel data to investigate in detail differential behaviour of

bank lending, especially with regard to their capital strength and asset

size.

Aggregate evidence show that a monetary policy is able to affect

bank lending with a lag due to ability of banks to insulate the decrease in

deposits by liquidating their securities holdings. This is conducted by bank

to serve the commitment loans that have been made prior to the

monetary shock. Empirical results with disaggregate data across bank

categories indicate that after a monetary shock, in particular in the period

of post crisis, there is a flight to quality of deposits especially from private

domestic banks to foreign banks and state banks. Accordingly, lending

of these categories of banks is less sensitive to a monetary shock

compared with that of private banks.

38

A disaggregation of total bank loans into corporate and individual

lending demonstrates that the response of aggregate lending to firms is

less sensitive to a monetary policy. By contrast, the loans for individuals

drop significantly in the aftermath of monetary shock. This may be

explained by what so-called the „flight to quality‟ phenomenon. That is, in

a monetary contraction, to compensate the decline in cash flow, the

creditworthy borrowers have access to short-term loans, while loans to the

less creditworthy borrowers, such as individuals, will be rationed. .

Disaggregation of banks according to their capital strength, we found

that the effect of monetary policy on bank lending is stronger for banks

with low capital. From time series and panel data estimations, the study

found that efficacy of a monetary policy in influencing the bank lending

and thus investment is stronger in the aftermath of the crisis, especially in

the case of monetary contraction. Ineffectiveness of monetary policy in

affecting the bank lending prior to the crisis was due to banks‟ ability to

access funds from international sources. In the wake of the crisis, given

deterioration of bank capital and high credit risk, an increase in interest

rate as a result of a monetary tightening raises the probability of loan

default, hence banks become reluctant to extend credits. This findings

lend support the existence of asymmetric effect of monetary policy;

stronger in the recession than in the boom periods.

39

References

Agung, Juda (1998). Financial deregulation and bank lending channel of

monetary policy in developing countries: The case of Indonesia, Asian

Economic Journal, Vol. 12, No.3, 273-294.

Agung, J., Bambang Kusmiarso, Bambang Pramono, Erwin G Hutapea,

Andry Pasmuko, Nugroho J Prastowo (2001). Credit Crunch in Indonesia in

the Aftermath of the Crisis: Facts, Causes and Policy Implications. Bank

Indonesia, Directorate of Economic Research and Monetary Policy.

Bernanke, B., 1993a, Credit in the macroeconomy. Fed Res Bank of New

York Quarterly Review, Spring 1993, pp. 50-70.

Bernanke, B., 1993b, How important is the credit channel in the

transmission of monetary policy? A comment, Carnegie-Rochester Series

on Public Policy, December 1993, pp. 47-52.

Bernanke, B., 1995, What do we know about how monetary policy affects

the economy, Fed Res Bank of St Louis Economics Review, May/June

1995, pp. 127-42.

Bernanke, B. and Blinder, A.S., 1988, Credit, Money and Aggregate

Demand, The American Economic Review , June 1983, pp. 257-76.

Bernanke, B. and Blinder, A.S., 1992, The federal funds rate and the

channel of monetary transmission, The American Economic Review

September 1992, pp. 901-21.

Bernanke, B. and Gertler, M., 1989, Agency cost, net worth and business

fluctuations,

The American Economic Review March 1989, pp. 14-31.

Bernanke, B and Gertler, M., 1995, Inside the black box: credit channel of

monetary transmission mechanism, Journal of Economic Perspective, Fall

1995, pp. 27-48.

Bernanke, B., Gertler M., and Gilchrist, S., 1996, The financial accelerator

and the flight to quality. Review of Economics and Statistics February

1996, pp. 1-15.

40

Callen, T. and Reynolds, P., 1996, Capital market development and

financial deregulation in Thailand and Malaysia: implications for financial

structure and the monetary transmission mechanism, Paper presented in

the Conference on Macroeconomic Issues Facing ASEAN Countries,

Jakarta.

Calomiris, C.W and Hubard, G., 1990, Firm heterogeneity, internal finance

and credit rationing. Economic Journal, 100, pp. 90-104.

Cecchetti, S.G., 1995, Distinguishing theories of the monetary transmission

mechanism, Fed Res Bank of St Louis Economic Review, May/June 1995,

pp. 83-100.

Cole, D. and Slade, B.S., 1990, Indonesian Financial Development: A

different sequencing? Harvard Institute for International Development

HIID, Harvard University, Cambridge, Massachusetts unpublished.

Cole, D. and Slade, B.S., 1996, Building a modern financial system: The

Indonesian experience, Cambridge University Press.

Dale, S and Haldane, H., 1994, Interest rates and the channel of

monetary transmission: Some sectoral estimates, European Economic

Review 39, pp. 1611-25.

Enders, W., 1995, Applied Econometric Time Series, John Wiley and Sons,

Inc.

Gertler, M and Gilchrist, S., 1993, The role of credit market imperfections in

the monetary trasmission mechanism: Arguments and evidence,

Scandinavian Journal of Economics, 951, pp. 43-64.

Gertler, M and Gilchrist, S., 1994, Monetary policy, business cycles and the

behaviour of small manufacturing firms, Quarterly Journal of Economics,

May 1994, pp. 309-40.

Himmelberg, C.P., and Morgan, D.P., 1995, Is bank lending special?, in

Peek, J., and Rosengren, E.S. (eds), Is bank lending important for the

transmission of monetary policy?, Federal Reserve Bank of Boston,

Conference Series No. 39, June 1995.

International Monetary Fund, 1995, Capital Flows to the Asia Pacific

Region, Occasional Paper No. 122. March 1995.

41

Kakes, J. (2000). Monetary Transmission in Europe. The Role of Financial

Markets and Credit. Edward Elgar: Cheltenham.

Kashyap, A.K. and Stein, J.C., 1995, Impact of monetary policy on bank

balance sheets. Carnegie-Rochester Series on Public Policy, June 1995,

pp. 151-95.

Kashyap, A.K. and Stein, J.C., (2000), What do a million banks have to say

about the transmission of monetary policy?, American Economic Review.

Peek, J and Rosengren, E. ,1995, The capital crunch: neither a borrower

nor a lender be, Journal of Money Credit and Banking, August 1995, pp.

625-38.

Ramey, V, 1993, How important is the credit channel in the transmission of

monetary policy?, Carnegie-Rochester Conference Series on Public

Policy, Dec 1993, pp. 1-45.

Romer, C.D. and Romer,D. ,1990, New evidence on the monetary

transmission mechanism, Brooking Papers on Economic Activity 1990:1,

pp. 149-213.

Spiegel, M, 1995, Sterilisation of capital inflows through the banking

sector: evidence from Asia, Federal Reserve Bank of San Fransisco

Economic Review, Vol 1995, No. 3, pp. 17-34.

42

Appendix A. Data: sources and definition

Data are monthly from January 1991 - December 2000.

1. Macroeconomic data:

SBI interest rate: It is the end-of-period 1-month SBI rates published

in Weekly Report of Bank Indonesia.

Money market (PUAB) interest rate: It is the end-of-period 1-month

interbank call money rates published in Weekly Report of Bank

Indonesia.

Real GDP: Monthly data of the real GDP is interpolated from

quarterly real GDP published in Indonesian Financial Statistics, Bank

Indonesia. The interpolation was performed by the piece-wise

cubic spline method. For early periods when the quarterly data

were not published officially, the data were obtained directly from

Indonesian Central Bureau of Statistics.

Production Index: Monthly data of the production index is

interpolated from quarterly production index published by Central

Bureau of Statistics. The interpolation was performed by the piece-

wise cubic spline method.

Prices: Consumer price index published in the Indonesian Financial

Statistics, Bank Indonesia.

43

2. Banks’ balance sheet data:

Banks‟ balance sheet data is obtained from Monthly Commercial Bank

Report.

Deposits: Consist of demand deposits, savings deposits and time

deposits both in Rupiah and foreign currency, excluded certificate

deposits.

Total loans: total loans extended by commercial banks both in

Rupiah and foreign currency.

Working capital loans: loans extended for firms‟ working capital

both in Rupiah and foreign currency.

Investment loans: loans extended for firms‟ investment both in

Rupiah and foreign currency.

Loans to individuals: loans extended to households mainly for

durable goods, real estate and credit cards.

Loans to firms: loans extended to private enterprises, in the form of

either working capital or investment loans.

43

Appendix B. Results for cointegration tests

AGGREGATE

1991:01 - 2000:12 1991:01 - 1997:07

RPUAB RSBI RPUAB RSBI

Rank p Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat

1 137,63** 57,42** 136,93** 54,60** 155,40** 58,02** 182,58** 67,75**

2 80,21** 34,84* 82,33** 38,13* 97,38** 35,89* 114,84** 44,23**

3 45,37 23,36 44,20 19,07 61,49** 27,63* 70,61** 36,50**

4 22,00 11,90 25,13 17,53 33,86* 19,67 34,11* 18,43

5 10,11 10,01 7,60 6,67 14,19 9,88 15,68* 11,08

6 0,09 0,09 0,93 0,93 4,31* 4,31* 4,59* 4,59*

STATE BANKS

1991:01 - 2000:12 1991:01 - 1997:07

RPUAB RSBI RPUAB RSBI

Rank p Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat

1 165,96** 65,94** 183,98** 67,45** 109,83** 44,24* 128,99** 49,69**

2 100,02** 39,73** 116,54** 51,09** 65,59 23,35 79,30** 35,15*

3 60,29** 30,35* 65,45** 35,45** 42,24 21,12 44,15 20,94

4 29,93* 21,96* 30,00* 23,85* 21,12 11,54 23,21 13,57

5 7,98 7,92 6,16 5,27 9,58 5,82 9,64 6,31

6 0,057 0,06 0,88 0,88 3,76* 3,76* 3,33 3,33

44

PRIVATE BANKS

1991:01 - 2000:12 1991:01 - 1997:07

RPUAB RSBI1M RPUAB RSBI1M

Rank p Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat

1 166,30** 84,91** 153,93** 64,18** 154,09** 52,83** 154,55** 48,76**

2 81,39** 36,11* 89,75** 59,07** 101,26** 39,84** 105,79** 40,25**

3 45,28 22,43 30,68 18,49 61,42** 27,77* 65,53** 34,44**

4 22,86 14,89 12,19 6,99 33,65* 22,15* 31,10* 17,99

5 7,96 7,93 5,19 5,11 11,50 8,34 13,11 7,56

6 0,03 0,03 0,08 0,08 3,16 3,16 5,54* 5,54*

FX-BANKS

1991:01 - 2000:12 1991:01 - 1997:07

RPUAB RSBI1M RPUAB RSBI1M

Rank p Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat

1 163,25** 82,01** 147,51** 70,14** 146,21** 45,17** 147,78** 41,25*

2 81,24** 33,92* 77,37** 49,14** 101,04** 36,19* 106,53** 37,80*

3 47,32* 24,33 28,23 13,38 64,86** 28,07* 68,73** 32,34**

4 22,98 16,39 14,85 9,66 36,79** 23,05* 36,39** 21,37*

5 6,59 6,52 5,19 4,91 13,73 11,69 15,02 11,43

6 0,07 0,07 0,28 0,28 2,05 2,05 3,59 3,59

45

FOREIGN BANKS

1991:01 - 2000:12 1991:01 - 1997:07

RPUAB RSBI1M RPUAB RSBI1M

Rank p Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat

1 122,64** 54,41** 131,23** 57,07** 148,73** 64,96** 147,66** 64,74**

2 68,23 29,46 74,16* 37,53* 83,77** 37,21* 82,91** 37,00*

3 38,77 17,93 36,64 18,21 46,56 27,83* 45,91 25,94

4 20,84 14,42 18,42 12,10 18,73 12,66 19,97 11,73

5 6,42 6,28 6,33 5,46 6,07 4,83 8,24 8,04

6 0,14 0,14 0,87 0,87 1,24 1,24 0,19 0,19

LOAN TO INDIVIDUALS

1991:01 - 2000:12 1991:01 - 1997:07

RPUAB RSBI1M RPUAB RSBI1M

Rank p Trace Stat Max-Eigen Stat Trace Stat

Max-Eigen

Stat

Trace

Stat

Max-Eigen

Stat

Trace

Stat Max-Eigen Stat

1 242,54** 110,94** 203,52** 66,96** 136,44** 50,09** 153,03** 46,44**

2 131,59** 57,24** 136,56** 63,29** 86,35** 35,77* 106,58** 41,62**

3 74,35** 38,33** 73,27** 39,70** 50,59* 25,53 64,95** 36,32**

4 36,02** 18,63 33,56* 18,69 25,05 18,31 28,63 22,10*

5 17,39* 16,83* 14,87 14,79* 6,74 6,55 6,52 6,52

6 0,57 0,57 0,07 0,07 0,19 0,19 0,00 0,00

46

LOAN TO PRIVATE ENTERPRISES

1991:01 - 2000:12 1991:01 - 1997:07

RPUAB RSBI1M RPUAB RSBI1M

Rank p Trace Stat Max-Eigen Stat Trace Stat

Max-Eigen

Stat Trace Stat Max-Eigen Stat Trace Stat

Max-Eigen

Stat

1 265,79** 109,59** 232,07** 89,59** 138,39** 42,63*

2 156,19** 76,11** 142,48** 78,46** 95,76** 40,34**

3 80,09** 50,18** 64,02** 32,36** 55,42** 27,64*

4 29,91* 20,37 31,66* 23,96* 27,78 18,06

5 9,55 9,47 0,07 7,66 9,72 8,28

6 0,08 0,08 0,00 0,05 1,44 1,44

INVESTMENT LOANS

1989:01 - 2000:12 1989:01 - 1997:07

RPUAB RSBI1M RPUAB RSBI1M

Rank p Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat

1 147,75** 54,86** 191,32** 73,13** 135,24** 59,84** 141,77** 63,61**

2 92,89** 36,37* 118,19** 43,00** 75,39* 33,33 78,15** 29,21

3 56,52** 23,77 75,19** 35,50** 42,06 19,31 48,94* 27,29*

4 32,75* 20,61 39,69** 21,27* 22,75 17,15 21,65 11,81

5 12,14 10,78 18,42* 13,26 5,59 4,65 9,84 5,65

6 1,36 1,36 5,15* 5,15* 0,94 0,94 4,19* 4,19*

47

WORKING CAPITAL LOANS

1989:01 - 2000:12 1989:01 - 1997:07

RPUAB RSBI1M RPUAB RSBI1M

Rank p Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat Trace Stat Max-Eigen Stat

1 272,01** 132,95** 222,94** 90,08** 196,10** 78,15** 190,79** 76,19**

2 139,06** 60,07** 132,86** 64,35* 117,95** 54,98** 114,59** 42,84**

3 78,98 49,89** 68,51** 36,25** 62,97** 36,31** 71,74** 39,27

4 29,09 20,63 32,26* 25,04* 26,67 16,89 32,47* 20,51

5 8,46 8,45 7,22 7,15 9,77 6,99 11,96 8,23

6 0,00 0,00 0,07 0,07 2,78 2,78 3,73 3,73